Optimization (:mod:`scipy.optimize`)
====================================

.. sectionauthor:: Travis E. Oliphant

.. sectionauthor:: Pauli Virtanen

.. sectionauthor:: Denis Laxalde

.. currentmodule:: scipy.optimize

The :mod:`scipy.optimize` package provides several commonly used
optimization algorithms. A detailed listing is available:
:mod:`scipy.optimize` (can also be found by ``help(scipy.optimize)``).

The module contains:

1. Unconstrained and constrained minimization of multivariate scalar
   functions (:func:`minimize`) using a variety of algorithms (e.g. BFGS,
   Nelder-Mead simplex, Newton Conjugate Gradient, COBYLA or SLSQP)

2. Global (brute-force) optimization routines  (e.g., :func:`anneal`, :func:`basinhopping`)

3. Least-squares minimization (:func:`leastsq`) and curve fitting
   (:func:`curve_fit`) algorithms

4. Scalar univariate functions minimizers (:func:`minimize_scalar`) and
   root finders (:func:`newton`)

5. Multivariate equation system solvers (:func:`root`) using a variety of
   algorithms (e.g. hybrid Powell, Levenberg-Marquardt or large-scale
   methods such as Newton-Krylov [KK]_).

Below, several examples demonstrate their basic usage.


Unconstrained minimization of multivariate scalar functions (:func:`minimize`)
------------------------------------------------------------------------------

The :func:`minimize` function provides a common interface to unconstrained
and constrained minimization algorithms for multivariate scalar functions
in `scipy.optimize`. To demonstrate the minimization function consider the
problem of minimizing the Rosenbrock function of :math:`N` variables:

.. math::
   :nowrap:

    \[ f\left(\mathbf{x}\right)=\sum_{i=1}^{N-1}100\left(x_{i}-x_{i-1}^{2}\right)^{2}+\left(1-x_{i-1}\right)^{2}.\]

The minimum value of this function is 0 which is achieved when
:math:`x_{i}=1.`

Note that the Rosenbrock function and its derivatives are included in
`scipy.optimize`. The implementations shown in the following sections
provide examples of how to define an objective function as well as its
jacobian and hessian functions.

Nelder-Mead Simplex algorithm (``method='Nelder-Mead'``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In the example below, the :func:`minimize` routine is used
with the *Nelder-Mead* simplex algorithm (selected through the ``method``
parameter):

    >>> import numpy as np
    >>> from scipy.optimize import minimize

    >>> def rosen(x):
    ...     """The Rosenbrock function"""
    ...     return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)

    >>> x0 = np.array([1.3, 0.7, 0.8, 1.9, 1.2])
    >>> res = minimize(rosen, x0, method='nelder-mead',
    ...                options={'xtol': 1e-8, 'disp': True})
    Optimization terminated successfully.
             Current function value: 0.000000
             Iterations: 339
             Function evaluations: 571

    >>> print(res.x)
    [ 1.  1.  1.  1.  1.]

The simplex algorithm is probably the simplest way to minimize a fairly
well-behaved function. It requires only function evaluations and is a good
choice for simple minimization problems. However, because it does not use
any gradient evaluations, it may take longer to find the minimum.

Another optimization algorithm that needs only function calls to find
the minimum is *Powell*'s method available by setting ``method='powell'`` in
:func:`minimize`.


Broyden-Fletcher-Goldfarb-Shanno algorithm (``method='BFGS'``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In order to converge more quickly to the solution, this routine uses
the gradient of the objective function. If the gradient is not given
by the user, then it is estimated using first-differences. The
Broyden-Fletcher-Goldfarb-Shanno (BFGS) method typically requires
fewer function calls than the simplex algorithm even when the gradient
must be estimated.

To demonstrate this algorithm, the Rosenbrock function is again used.
The gradient of the Rosenbrock function is the vector:

.. math::
   :nowrap:

    \begin{eqnarray*} \frac{\partial f}{\partial x_{j}} & = & \sum_{i=1}^{N}200\left(x_{i}-x_{i-1}^{2}\right)\left(\delta_{i,j}-2x_{i-1}\delta_{i-1,j}\right)-2\left(1-x_{i-1}\right)\delta_{i-1,j}.\\  & = & 200\left(x_{j}-x_{j-1}^{2}\right)-400x_{j}\left(x_{j+1}-x_{j}^{2}\right)-2\left(1-x_{j}\right).\end{eqnarray*}

This expression is valid for the interior derivatives. Special cases
are

.. math::
   :nowrap:

    \begin{eqnarray*} \frac{\partial f}{\partial x_{0}} & = & -400x_{0}\left(x_{1}-x_{0}^{2}\right)-2\left(1-x_{0}\right),\\ \frac{\partial f}{\partial x_{N-1}} & = & 200\left(x_{N-1}-x_{N-2}^{2}\right).\end{eqnarray*}

A Python function which computes this gradient is constructed by the
code-segment:

    >>> def rosen_der(x):
    ...     xm = x[1:-1]
    ...     xm_m1 = x[:-2]
    ...     xm_p1 = x[2:]
    ...     der = np.zeros_like(x)
    ...     der[1:-1] = 200*(xm-xm_m1**2) - 400*(xm_p1 - xm**2)*xm - 2*(1-xm)
    ...     der[0] = -400*x[0]*(x[1]-x[0]**2) - 2*(1-x[0])
    ...     der[-1] = 200*(x[-1]-x[-2]**2)
    ...     return der

This gradient information is specified in the :func:`minimize` function
through the ``jac`` parameter as illustrated below.


    >>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
    ...                options={'disp': True})
    Optimization terminated successfully.
             Current function value: 0.000000
             Iterations: 51
             Function evaluations: 63
             Gradient evaluations: 63
    >>> print(res.x)
    [ 1.  1.  1.  1.  1.]


Newton-Conjugate-Gradient algorithm (``method='Newton-CG'``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The method which requires the fewest function calls and is therefore often
the fastest method to minimize functions of many variables uses the
Newton-Conjugate Gradient algorithm. This method is a modified Newton's
method and uses a conjugate gradient algorithm to (approximately) invert
the local Hessian.  Newton's method is based on fitting the function
locally to a quadratic form:

.. math::
   :nowrap:

    \[ f\left(\mathbf{x}\right)\approx f\left(\mathbf{x}_{0}\right)+\nabla f\left(\mathbf{x}_{0}\right)\cdot\left(\mathbf{x}-\mathbf{x}_{0}\right)+\frac{1}{2}\left(\mathbf{x}-\mathbf{x}_{0}\right)^{T}\mathbf{H}\left(\mathbf{x}_{0}\right)\left(\mathbf{x}-\mathbf{x}_{0}\right).\]

where :math:`\mathbf{H}\left(\mathbf{x}_{0}\right)` is a matrix of second-derivatives (the Hessian). If the Hessian is
positive definite then the local minimum of this function can be found
by setting the gradient of the quadratic form to zero, resulting in

.. math::
   :nowrap:

    \[ \mathbf{x}_{\textrm{opt}}=\mathbf{x}_{0}-\mathbf{H}^{-1}\nabla f.\]

The inverse of the Hessian is evaluated using the conjugate-gradient
method. An example of employing this method to minimizing the
Rosenbrock function is given below. To take full advantage of the
Newton-CG method, a function which computes the Hessian must be
provided. The Hessian matrix itself does not need to be constructed,
only a vector which is the product of the Hessian with an arbitrary
vector needs to be available to the minimization routine. As a result,
the user can provide either a function to compute the Hessian matrix,
or a function to compute the product of the Hessian with an arbitrary
vector.


Full Hessian example:
"""""""""""""""""""""

The Hessian of the Rosenbrock function is

.. math::
   :nowrap:

    \begin{eqnarray*} H_{ij}=\frac{\partial^{2}f}{\partial x_{i}\partial x_{j}} & = & 200\left(\delta_{i,j}-2x_{i-1}\delta_{i-1,j}\right)-400x_{i}\left(\delta_{i+1,j}-2x_{i}\delta_{i,j}\right)-400\delta_{i,j}\left(x_{i+1}-x_{i}^{2}\right)+2\delta_{i,j},\\  & = & \left(202+1200x_{i}^{2}-400x_{i+1}\right)\delta_{i,j}-400x_{i}\delta_{i+1,j}-400x_{i-1}\delta_{i-1,j},\end{eqnarray*}

if :math:`i,j\in\left[1,N-2\right]` with :math:`i,j\in\left[0,N-1\right]` defining the :math:`N\times N` matrix. Other non-zero entries of the matrix are

.. math::
   :nowrap:

    \begin{eqnarray*} \frac{\partial^{2}f}{\partial x_{0}^{2}} & = & 1200x_{0}^{2}-400x_{1}+2,\\ \frac{\partial^{2}f}{\partial x_{0}\partial x_{1}}=\frac{\partial^{2}f}{\partial x_{1}\partial x_{0}} & = & -400x_{0},\\ \frac{\partial^{2}f}{\partial x_{N-1}\partial x_{N-2}}=\frac{\partial^{2}f}{\partial x_{N-2}\partial x_{N-1}} & = & -400x_{N-2},\\ \frac{\partial^{2}f}{\partial x_{N-1}^{2}} & = & 200.\end{eqnarray*}

For example, the Hessian when :math:`N=5` is

.. math::
   :nowrap:

    \[ \mathbf{H}=\left[\begin{array}{ccccc} 1200x_{0}^{2}-400x_{1}+2 & -400x_{0} & 0 & 0 & 0\\ -400x_{0} & 202+1200x_{1}^{2}-400x_{2} & -400x_{1} & 0 & 0\\ 0 & -400x_{1} & 202+1200x_{2}^{2}-400x_{3} & -400x_{2} & 0\\ 0 &  & -400x_{2} & 202+1200x_{3}^{2}-400x_{4} & -400x_{3}\\ 0 & 0 & 0 & -400x_{3} & 200\end{array}\right].\]

The code which computes this Hessian along with the code to minimize
the function using Newton-CG method is shown in the following example:

    >>> def rosen_hess(x):
    ...     x = np.asarray(x)
    ...     H = np.diag(-400*x[:-1],1) - np.diag(400*x[:-1],-1)
    ...     diagonal = np.zeros_like(x)
    ...     diagonal[0] = 1200*x[0]**2-400*x[1]+2
    ...     diagonal[-1] = 200
    ...     diagonal[1:-1] = 202 + 1200*x[1:-1]**2 - 400*x[2:]
    ...     H = H + np.diag(diagonal)
    ...     return H

    >>> res = minimize(rosen, x0, method='Newton-CG',
    ...                jac=rosen_der, hess=rosen_hess,
    ...                options={'avextol': 1e-8, 'disp': True})
    Optimization terminated successfully.
             Current function value: 0.000000
             Iterations: 19
             Function evaluations: 22
             Gradient evaluations: 19
             Hessian evaluations: 19
    >>> print(res.x)
    [ 1.  1.  1.  1.  1.]


Hessian product example:
""""""""""""""""""""""""

For larger minimization problems, storing the entire Hessian matrix can
consume considerable time and memory. The Newton-CG algorithm only needs
the product of the Hessian times an arbitrary vector. As a result, the user
can supply code to compute this product rather than the full Hessian by
giving a ``hess`` function which take the minimization vector as the first
argument and the arbitrary vector as the second argument (along with extra
arguments passed to the function to be minimized). If possible, using
Newton-CG with the Hessian product option is probably the fastest way to
minimize the function.

In this case, the product of the Rosenbrock Hessian with an arbitrary
vector is not difficult to compute. If :math:`\mathbf{p}` is the arbitrary
vector, then :math:`\mathbf{H}\left(\mathbf{x}\right)\mathbf{p}` has
elements:

.. math::
   :nowrap:

    \[ \mathbf{H}\left(\mathbf{x}\right)\mathbf{p}=\left[\begin{array}{c} \left(1200x_{0}^{2}-400x_{1}+2\right)p_{0}-400x_{0}p_{1}\\ \vdots\\ -400x_{i-1}p_{i-1}+\left(202+1200x_{i}^{2}-400x_{i+1}\right)p_{i}-400x_{i}p_{i+1}\\ \vdots\\ -400x_{N-2}p_{N-2}+200p_{N-1}\end{array}\right].\]

Code which makes use of this Hessian product to minimize the
Rosenbrock function using :func:`minimize` follows:

    >>> def rosen_hess_p(x,p):
    ...     x = np.asarray(x)
    ...     Hp = np.zeros_like(x)
    ...     Hp[0] = (1200*x[0]**2 - 400*x[1] + 2)*p[0] - 400*x[0]*p[1]
    ...     Hp[1:-1] = -400*x[:-2]*p[:-2]+(202+1200*x[1:-1]**2-400*x[2:])*p[1:-1] \
    ...                -400*x[1:-1]*p[2:]
    ...     Hp[-1] = -400*x[-2]*p[-2] + 200*p[-1]
    ...     return Hp

    >>> res = minimize(rosen, x0, method='Newton-CG',
    ...                jac=rosen_der, hess=rosen_hess_p,
    ...                options={'avextol': 1e-8, 'disp': True})
    Optimization terminated successfully.
             Current function value: 0.000000
             Iterations: 20
             Function evaluations: 23
             Gradient evaluations: 20
             Hessian evaluations: 44
    >>> print(res.x)
    [ 1.  1.  1.  1.  1.]


.. _tutorial-sqlsp:

Constrained minimization of multivariate scalar functions (:func:`minimize`)
----------------------------------------------------------------------------

The :func:`minimize` function also provides an interface to several
constrained minimization algorithm. As an example, the Sequential Least
SQuares Programming optimization algorithm (SLSQP) will be considered here.
This algorithm allows to deal with constrained minimization problems of the
form:

.. math::
   :nowrap:

     \begin{eqnarray*} \min F(x) \\ \text{subject to } & C_j(X) =  0  ,  &j = 1,...,\text{MEQ}\\
            & C_j(x) \geq 0  ,  &j = \text{MEQ}+1,...,M\\
           &  XL  \leq x \leq XU , &I = 1,...,N. \end{eqnarray*}


As an example, let us consider the problem of maximizing the function:

.. math::
    :nowrap:

    \[ f(x, y) = 2 x y + 2 x - x^2 - 2 y^2 \]

subject to an equality and an inequality constraints defined as:

.. math::
    :nowrap:

    \[ x^3 - y = 0 \]
    \[ y - 1 \geq 0 \]



The objective function and its derivative are defined as follows.

    >>> def func(x, sign=1.0):
    ...     """ Objective function """
    ...     return sign*(2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2)

    >>> def func_deriv(x, sign=1.0):
    ...     """ Derivative of objective function """
    ...     dfdx0 = sign*(-2*x[0] + 2*x[1] + 2)
    ...     dfdx1 = sign*(2*x[0] - 4*x[1])
    ...     return np.array([ dfdx0, dfdx1 ])

Note that since :func:`minimize` only minimizes functions, the ``sign``
parameter is introduced to multiply the objective function (and its
derivative by -1) in order to perform a maximization.

Then constraints are defined as a sequence of dictionaries, with keys
``type``, ``fun`` and ``jac``.

    >>> cons = ({'type': 'eq',
    ...          'fun' : lambda x: np.array([x[0]**3 - x[1]]),
    ...          'jac' : lambda x: np.array([3.0*(x[0]**2.0), -1.0])},
    ...         {'type': 'ineq',
    ...          'fun' : lambda x: np.array([x[1] - 1]),
    ...          'jac' : lambda x: np.array([0.0, 1.0])})


Now an unconstrained optimization can be performed as:

    >>> res = minimize(func, [-1.0,1.0], args=(-1.0,), jac=func_deriv,
    ...                method='SLSQP', options={'disp': True})
    Optimization terminated successfully.    (Exit mode 0)
                Current function value: -2.0
                Iterations: 4
                Function evaluations: 5
                Gradient evaluations: 4
    >>> print(res.x)
    [ 2.  1.]

and a constrained optimization as:

    >>> res = minimize(func, [-1.0,1.0], args=(-1.0,), jac=func_deriv,
    ...                constraints=cons, method='SLSQP', options={'disp': True})
    Optimization terminated successfully.    (Exit mode 0)
                Current function value: -1.00000018311
                Iterations: 9
                Function evaluations: 14
                Gradient evaluations: 9
    >>> print(res.x)
    [ 1.00000009  1.        ]


Least-square fitting (:func:`leastsq`)
--------------------------------------

All of the previously-explained minimization procedures can be used to
solve a least-squares problem provided the appropriate objective
function is constructed. For example, suppose it is desired to fit a
set of data :math:`\left\{\mathbf{x}_{i}, \mathbf{y}_{i}\right\}`
to a known model,
:math:`\mathbf{y}=\mathbf{f}\left(\mathbf{x},\mathbf{p}\right)`
where :math:`\mathbf{p}` is a vector of parameters for the model that
need to be found. A common method for determining which parameter
vector gives the best fit to the data is to minimize the sum of squares
of the residuals. The residual is usually defined for each observed
data-point as

.. math::
   :nowrap:

    \[ e_{i}\left(\mathbf{p},\mathbf{y}_{i},\mathbf{x}_{i}\right)=\left\Vert \mathbf{y}_{i}-\mathbf{f}\left(\mathbf{x}_{i},\mathbf{p}\right)\right\Vert .\]

An objective function to pass to any of the previous minization
algorithms to obtain a least-squares fit is.

.. math::
   :nowrap:

    \[ J\left(\mathbf{p}\right)=\sum_{i=0}^{N-1}e_{i}^{2}\left(\mathbf{p}\right).\]



The :obj:`leastsq` algorithm performs this squaring and summing of the
residuals automatically. It takes as an input argument the vector
function :math:`\mathbf{e}\left(\mathbf{p}\right)` and returns the
value of :math:`\mathbf{p}` which minimizes
:math:`J\left(\mathbf{p}\right)=\mathbf{e}^{T}\mathbf{e}`
directly. The user is also encouraged to provide the Jacobian matrix
of the function (with derivatives down the columns or across the
rows). If the Jacobian is not provided, it is estimated.

An example should clarify the usage. Suppose it is believed some
measured data follow a sinusoidal pattern

.. math::
   :nowrap:

    \[ y_{i}=A\sin\left(2\pi kx_{i}+\theta\right)\]

where the parameters :math:`A,` :math:`k` , and :math:`\theta` are unknown. The residual vector is

.. math::
   :nowrap:

    \[ e_{i}=\left|y_{i}-A\sin\left(2\pi kx_{i}+\theta\right)\right|.\]

By defining a function to compute the residuals and (selecting an
appropriate starting position), the least-squares fit routine can be
used to find the best-fit parameters :math:`\hat{A},\,\hat{k},\,\hat{\theta}`.
This is shown in the following example:

.. plot::

   >>> from numpy import *
   >>> x = arange(0,6e-2,6e-2/30)
   >>> A,k,theta = 10, 1.0/3e-2, pi/6
   >>> y_true = A*sin(2*pi*k*x+theta)
   >>> y_meas = y_true + 2*random.randn(len(x))

   >>> def residuals(p, y, x):
   ...     A,k,theta = p
   ...     err = y-A*sin(2*pi*k*x+theta)
   ...     return err

   >>> def peval(x, p):
   ...     return p[0]*sin(2*pi*p[1]*x+p[2])

   >>> p0 = [8, 1/2.3e-2, pi/3]
   >>> print(array(p0))
   [  8.      43.4783   1.0472]

   >>> from scipy.optimize import leastsq
   >>> plsq = leastsq(residuals, p0, args=(y_meas, x))
   >>> print(plsq[0])
   [ 10.9437  33.3605   0.5834]

   >>> print(array([A, k, theta]))
   [ 10.      33.3333   0.5236]

   >>> import matplotlib.pyplot as plt
   >>> plt.plot(x,peval(x,plsq[0]),x,y_meas,'o',x,y_true)
   >>> plt.title('Least-squares fit to noisy data')
   >>> plt.legend(['Fit', 'Noisy', 'True'])
   >>> plt.show()

..   :caption: Least-square fitting to noisy data using
..             :obj:`scipy.optimize.leastsq`


Univariate function minimizers (:func:`minimize_scalar`)
--------------------------------------------------------

Often only the minimum of an univariate function (i.e. a function that
takes a scalar as input) is needed. In these circumstances, other
optimization techniques have been developed that can work faster. These are
accessible from the :func:`minimize_scalar` function which proposes several
algorithms.


Unconstrained minimization (``method='brent'``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

There are actually two methods that can be used to minimize an univariate
function: `brent` and `golden`, but `golden` is included only for academic
purposes and should rarely be used. These can be respectively selected
through the `method` parameter in :func:`minimize_scalar`. The `brent`
method uses Brent's algorithm for locating a minimum. Optimally a bracket
(the `bs` parameter) should be given which contains the minimum desired. A
bracket is a triple :math:`\left( a, b, c \right)` such that :math:`f
\left( a \right) > f \left( b \right) < f \left( c \right)` and :math:`a <
b < c` . If this is not given, then alternatively two starting points can
be chosen and a bracket will be found from these points using a simple
marching algorithm. If these two starting points are not provided `0` and
`1` will be used (this may not be the right choice for your function and
result in an unexpected minimum being returned).

Here is an example:

    >>> from scipy.optimize import minimize_scalar
    >>> f = lambda x: (x - 2) * (x + 1)**2
    >>> res = minimize_scalar(f, method='brent')
    >>> print(res.x)
    1.0


Bounded minimization (``method='bounded'``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Very often, there are constraints that can be placed on the solution space
before minimization occurs. The `bounded` method in :func:`minimize_scalar`
is an example of a constrained minimization procedure that provides a
rudimentary interval constraint for scalar functions. The interval
constraint allows the minimization to occur only between two fixed
endpoints, specified using the mandatory `bs` parameter.

For example, to find the minimum of :math:`J_{1}\left( x \right)` near
:math:`x=5` , :func:`minimize_scalar` can be called using the interval
:math:`\left[ 4, 7 \right]` as a constraint. The result is
:math:`x_{\textrm{min}}=5.3314` :

    >>> from scipy.special import j1
    >>> res = minimize_scalar(j1, bs=(4, 7), method='bounded')
    >>> print(res.x)
    5.33144184241


Root finding
------------

Scalar functions
^^^^^^^^^^^^^^^^

If one has a single-variable equation, there are four different root
finding algorithms that can be tried. Each of these algorithms requires the
endpoints of an interval in which a root is expected (because the function
changes signs). In general :obj:`brentq` is the best choice, but the other
methods may be useful in certain circumstances or for academic purposes.


Fixed-point solving
^^^^^^^^^^^^^^^^^^^

A problem closely related to finding the zeros of a function is the
problem of finding a fixed-point of a function. A fixed point of a
function is the point at which evaluation of the function returns the
point: :math:`g\left(x\right)=x.` Clearly the fixed point of :math:`g`
is the root of :math:`f\left(x\right)=g\left(x\right)-x.`
Equivalently, the root of :math:`f` is the fixed_point of
:math:`g\left(x\right)=f\left(x\right)+x.` The routine
:obj:`fixed_point` provides a simple iterative method using Aitkens
sequence acceleration to estimate the fixed point of :math:`g` given a
starting point.

Sets of equations
^^^^^^^^^^^^^^^^^

Finding a root of a set of non-linear equations can be achieve using the
:func:`root` function. Several methods are available, amongst which ``hybr``
(the default) and ``lm`` which respectively use the hybrid method of Powell
and the Levenberg-Marquardt method from MINPACK.

The following example considers the single-variable transcendental
equation

.. math::
   :nowrap:

    \[ x+2\cos\left(x\right)=0,\]

a root of which can be found as follows::

    >>> import numpy as np
    >>> from scipy.optimize import root
    >>> def func(x):
    ...     return x + 2 * np.cos(x)
    >>> sol = root(func, 0.3)
    >>> sol.x
    array([-1.02986653])
    >>> sol.fun
    array([ -6.66133815e-16])

Consider now a set of non-linear equations

.. math::
   :nowrap:

    \begin{eqnarray*}
    x_{0}\cos\left(x_{1}\right) & = & 4,\\
    x_{0}x_{1}-x_{1} & = & 5.
    \end{eqnarray*}

We define the objective function so that it also returns the Jacobian and
indicate this by setting the ``jac`` parameter to ``True``. Also, the
Levenberg-Marquardt solver is used here.

::

    >>> def func2(x):
    ...     f = [x[0] * np.cos(x[1]) - 4,
    ...          x[1]*x[0] - x[1] - 5]
    ...     df = np.array([[np.cos(x[1]), -x[0] * np.sin(x[1])],
    ...                    [x[1], x[0] - 1]])
    ...     return f, df
    >>> sol = root(func2, [1, 1], jac=True, method='lm')
    >>> sol.x
    array([ 6.50409711,  0.90841421])


Root finding for large problems
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Methods ``hybr`` and ``lm`` in :func:`root` cannot deal with a very large
number of variables (*N*), as they need to calculate and invert a dense *N
x N* Jacobian matrix on every Newton step. This becomes rather inefficient
when *N* grows.

Consider for instance the following problem: we need to solve the
following integrodifferential equation on the square
:math:`[0,1]\times[0,1]`:

.. math::

   (\partial_x^2 + \partial_y^2) P + 5 \left(\int_0^1\int_0^1\cosh(P)\,dx\,dy\right)^2 = 0

with the boundary condition :math:`P(x,1) = 1` on the upper edge and
:math:`P=0` elsewhere on the boundary of the square. This can be done
by approximating the continuous function *P* by its values on a grid,
:math:`P_{n,m}\approx{}P(n h, m h)`, with a small grid spacing
*h*. The derivatives and integrals can then be approximated; for
instance :math:`\partial_x^2 P(x,y)\approx{}(P(x+h,y) - 2 P(x,y) +
P(x-h,y))/h^2`. The problem is then equivalent to finding the root of
some function ``residual(P)``, where ``P`` is a vector of length
:math:`N_x N_y`.

Now, because :math:`N_x N_y` can be large, methods ``hybr`` or ``lm`` in
:func:`root` will take a long time to solve this problem. The solution can
however be found using one of the large-scale solvers, for example
``krylov``, ``broyden2``, or ``anderson``. These use what is known as the
inexact Newton method, which instead of computing the Jacobian matrix
exactly, forms an approximation for it.

The problem we have can now be solved as follows:

.. plot::

    import numpy as np
    from scipy.optimize import root
    from numpy import cosh, zeros_like, mgrid, zeros

    # parameters
    nx, ny = 75, 75
    hx, hy = 1./(nx-1), 1./(ny-1)

    P_left, P_right = 0, 0
    P_top, P_bottom = 1, 0

    def residual(P):
       d2x = zeros_like(P)
       d2y = zeros_like(P)

       d2x[1:-1] = (P[2:]   - 2*P[1:-1] + P[:-2]) / hx/hx
       d2x[0]    = (P[1]    - 2*P[0]    + P_left)/hx/hx
       d2x[-1]   = (P_right - 2*P[-1]   + P[-2])/hx/hx

       d2y[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2])/hy/hy
       d2y[:,0]    = (P[:,1]  - 2*P[:,0]    + P_bottom)/hy/hy
       d2y[:,-1]   = (P_top   - 2*P[:,-1]   + P[:,-2])/hy/hy

       return d2x + d2y + 5*cosh(P).mean()**2

    # solve
    guess = zeros((nx, ny), float)
    sol = root(residual, guess, method='krylov', options={'disp': True})
    #sol = root(residual, guess, method='broyden2', options={'disp': True, 'max_rank': 50})
    #sol = root(residual, guess, method='anderson', options={'disp': True, 'M': 10})
    print('Residual: %g' % abs(residual(sol.x)).max())

    # visualize
    import matplotlib.pyplot as plt
    x, y = mgrid[0:1:(nx*1j), 0:1:(ny*1j)]
    plt.pcolor(x, y, sol.x)
    plt.colorbar()
    plt.show()


Still too slow? Preconditioning.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

When looking for the zero of the functions :math:`f_i({\bf x}) = 0`,
*i = 1, 2, ..., N*, the ``krylov`` solver spends most of its
time inverting the Jacobian matrix,

.. math:: J_{ij} = \frac{\partial f_i}{\partial x_j} .

If you have an approximation for the inverse matrix
:math:`M\approx{}J^{-1}`, you can use it for *preconditioning* the
linear inversion problem. The idea is that instead of solving
:math:`J{\bf s}={\bf y}` one solves :math:`MJ{\bf s}=M{\bf y}`: since
matrix :math:`MJ` is "closer" to the identity matrix than :math:`J`
is, the equation should be easier for the Krylov method to deal with.

The matrix *M* can be passed to :func:`root` with method ``krylov`` as an
option ``options['jac_options']['inner_M']``. It can be a (sparse) matrix
or a :obj:`scipy.sparse.linalg.LinearOperator` instance.

For the problem in the previous section, we note that the function to
solve consists of two parts: the first one is application of the
Laplace operator, :math:`[\partial_x^2 + \partial_y^2] P`, and the second
is the integral. We can actually easily compute the Jacobian corresponding
to the Laplace operator part: we know that in one dimension

.. math::

   \partial_x^2 \approx \frac{1}{h_x^2} \begin{pmatrix}
   -2 & 1 & 0 & 0 \cdots \\
   1 & -2 & 1 & 0 \cdots \\
   0 & 1 & -2 & 1 \cdots \\
   \ldots
   \end{pmatrix}
   = h_x^{-2} L

so that the whole 2-D operator is represented by

.. math::

   J_1 = \partial_x^2 + \partial_y^2
   \simeq
   h_x^{-2} L \otimes I + h_y^{-2} I \otimes L

The matrix :math:`J_2` of the Jacobian corresponding to the integral
is more difficult to calculate, and since *all* of it entries are
nonzero, it will be difficult to invert. :math:`J_1` on the other hand
is a relatively simple matrix, and can be inverted by
:obj:`scipy.sparse.linalg.splu` (or the inverse can be approximated by
:obj:`scipy.sparse.linalg.spilu`). So we are content to take
:math:`M\approx{}J_1^{-1}` and hope for the best.

In the example below, we use the preconditioner :math:`M=J_1^{-1}`.

.. literalinclude:: examples/newton_krylov_preconditioning.py

Resulting run, first without preconditioning::

  0:  |F(x)| = 803.614; step 1; tol 0.000257947
  1:  |F(x)| = 345.912; step 1; tol 0.166755
  2:  |F(x)| = 139.159; step 1; tol 0.145657
  3:  |F(x)| = 27.3682; step 1; tol 0.0348109
  4:  |F(x)| = 1.03303; step 1; tol 0.00128227
  5:  |F(x)| = 0.0406634; step 1; tol 0.00139451
  6:  |F(x)| = 0.00344341; step 1; tol 0.00645373
  7:  |F(x)| = 0.000153671; step 1; tol 0.00179246
  8:  |F(x)| = 6.7424e-06; step 1; tol 0.00173256
  Residual 3.57078908664e-07
  Evaluations 317

and then with preconditioning::

  0:  |F(x)| = 136.993; step 1; tol 7.49599e-06
  1:  |F(x)| = 4.80983; step 1; tol 0.00110945
  2:  |F(x)| = 0.195942; step 1; tol 0.00149362
  3:  |F(x)| = 0.000563597; step 1; tol 7.44604e-06
  4:  |F(x)| = 1.00698e-09; step 1; tol 2.87308e-12
  Residual 9.29603061195e-11
  Evaluations 77

Using a preconditioner reduced the number of evaluations of the
``residual`` function by a factor of *4*. For problems where the
residual is expensive to compute, good preconditioning can be crucial
--- it can even decide whether the problem is solvable in practice or
not.

Preconditioning is an art, science, and industry. Here, we were lucky
in making a simple choice that worked reasonably well, but there is a
lot more depth to this topic than is shown here.

.. rubric:: References

Some further reading and related software for solving large-scale problems
( [PP]_, [AMG]_):

.. [KK] D.A. Knoll and D.E. Keyes, "Jacobian-free Newton-Krylov methods",
        J. Comp. Phys. 193, 357 (2003).

.. [PP] PETSc http://www.mcs.anl.gov/petsc/ and its Python bindings
        http://code.google.com/p/petsc4py/

.. [AMG] PyAMG (algebraic multigrid preconditioners/solvers)
         http://code.google.com/p/pyamg/